Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations75
Missing cells75
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 KiB
Average record size in memory373.0 B

Variable types

Numeric15
Categorical11
DateTime1
Unsupported1

Alerts

Campaign_5 has constant value "0" Constant
Complaint_Flag has constant value "0" Constant
Contact_Cost has constant value "3" Constant
Total_Revenue has constant value "11" Constant
Annual_Income is highly overall correlated with Catalog_Orders and 8 other fieldsHigh correlation
Campaign_1 is highly overall correlated with Spent_GoldHigh correlation
Campaign_2 is highly overall correlated with Web_OrdersHigh correlation
Campaign_4 is highly overall correlated with Spent_Fish and 3 other fieldsHigh correlation
Catalog_Orders is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Spent_Fish is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Spent_Fruits is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Gold is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Meat is highly overall correlated with Annual_Income and 10 other fieldsHigh correlation
Spent_Sweets is highly overall correlated with Catalog_Orders and 4 other fieldsHigh correlation
Spent_Wines is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Store_Orders is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Web_Orders is highly overall correlated with Annual_Income and 6 other fieldsHigh correlation
Web_Visits is highly overall correlated with Annual_Income and 5 other fieldsHigh correlation
Campaign_1 is highly imbalanced (59.8%) Imbalance
Campaign_2 is highly imbalanced (64.7%) Imbalance
Campaign_4 is highly imbalanced (64.7%) Imbalance
Campaign_3 has 75 (100.0%) missing values Missing
User_Key has unique values Unique
Annual_Income has unique values Unique
Campaign_3 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Last_Visit has 2 (2.7%) zeros Zeros
Spent_Wines has 1 (1.3%) zeros Zeros
Spent_Fruits has 17 (22.7%) zeros Zeros
Spent_Fish has 13 (17.3%) zeros Zeros
Spent_Sweets has 16 (21.3%) zeros Zeros
Spent_Gold has 1 (1.3%) zeros Zeros
Web_Orders has 1 (1.3%) zeros Zeros
Catalog_Orders has 21 (28.0%) zeros Zeros

Reproduction

Analysis started2025-07-25 16:44:48.731719
Analysis finished2025-07-25 16:45:22.992873
Duration34.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

User_Key
Real number (ℝ)

Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5624.4267
Minimum550
Maximum10906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:23.323686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum550
5-th percentile789.8
Q13075
median5331
Q38428
95-th percentile10545.6
Maximum10906
Range10356
Interquartile range (IQR)5353

Descriptive statistics

Standard deviation3098.2189
Coefficient of variation (CV)0.55085062
Kurtosis-1.1208389
Mean5624.4267
Median Absolute Deviation (MAD)2719
Skewness0.124121
Sum421832
Variance9598960.4
MonotonicityNot monotonic
2025-07-25T21:45:23.598360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9370 1
 
1.3%
4682 1
 
1.3%
4530 1
 
1.3%
8212 1
 
1.3%
6409 1
 
1.3%
9058 1
 
1.3%
4299 1
 
1.3%
10413 1
 
1.3%
1890 1
 
1.3%
8414 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
550 1
1.3%
574 1
1.3%
615 1
1.3%
675 1
1.3%
839 1
1.3%
1041 1
1.3%
1045 1
1.3%
1404 1
1.3%
1890 1
1.3%
2156 1
1.3%
ValueCountFrequency (%)
10906 1
1.3%
10837 1
1.3%
10821 1
1.3%
10708 1
1.3%
10476 1
1.3%
10413 1
1.3%
10129 1
1.3%
10104 1
1.3%
9938 1
1.3%
9905 1
1.3%

Birth_Year
Real number (ℝ)

Distinct34
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.1733
Minimum1945
Maximum1991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:23.844637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1948.7
Q11956.5
median1969
Q31974
95-th percentile1984.3
Maximum1991
Range46
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation11.127484
Coefficient of variation (CV)0.0056594624
Kurtosis-0.81922252
Mean1966.1733
Median Absolute Deviation (MAD)9
Skewness0.044075034
Sum147463
Variance123.8209
MonotonicityNot monotonic
2025-07-25T21:45:24.072089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1971 7
 
9.3%
1974 5
 
6.7%
1978 4
 
5.3%
1958 4
 
5.3%
1960 4
 
5.3%
1956 4
 
5.3%
1952 3
 
4.0%
1975 3
 
4.0%
1969 3
 
4.0%
1955 3
 
4.0%
Other values (24) 35
46.7%
ValueCountFrequency (%)
1945 1
 
1.3%
1948 3
4.0%
1949 1
 
1.3%
1950 2
2.7%
1951 1
 
1.3%
1952 3
4.0%
1954 1
 
1.3%
1955 3
4.0%
1956 4
5.3%
1957 2
2.7%
ValueCountFrequency (%)
1991 1
 
1.3%
1988 1
 
1.3%
1987 1
 
1.3%
1985 1
 
1.3%
1984 1
 
1.3%
1983 2
 
2.7%
1978 4
5.3%
1977 2
 
2.7%
1975 3
4.0%
1974 5
6.7%

Edu_Level
Categorical

Distinct5
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
Graduation
36 
PhD
14 
Master
14 
2n Cycle
Basic
 
2

Length

Max length10
Median length8
Mean length7.5733333
Min length3

Characters and Unicode

Total characters568
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhD
2nd rowGraduation
3rd rowMaster
4th rowMaster
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 36
48.0%
PhD 14
 
18.7%
Master 14
 
18.7%
2n Cycle 9
 
12.0%
Basic 2
 
2.7%

Length

2025-07-25T21:45:24.298741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:24.488668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation 36
42.9%
phd 14
 
16.7%
master 14
 
16.7%
2n 9
 
10.7%
cycle 9
 
10.7%
basic 2
 
2.4%

Most occurring characters

ValueCountFrequency (%)
a 88
15.5%
r 50
 
8.8%
t 50
 
8.8%
n 45
 
7.9%
i 38
 
6.7%
G 36
 
6.3%
u 36
 
6.3%
d 36
 
6.3%
o 36
 
6.3%
e 23
 
4.0%
Other values (12) 130
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 88
15.5%
r 50
 
8.8%
t 50
 
8.8%
n 45
 
7.9%
i 38
 
6.7%
G 36
 
6.3%
u 36
 
6.3%
d 36
 
6.3%
o 36
 
6.3%
e 23
 
4.0%
Other values (12) 130
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 88
15.5%
r 50
 
8.8%
t 50
 
8.8%
n 45
 
7.9%
i 38
 
6.7%
G 36
 
6.3%
u 36
 
6.3%
d 36
 
6.3%
o 36
 
6.3%
e 23
 
4.0%
Other values (12) 130
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 88
15.5%
r 50
 
8.8%
t 50
 
8.8%
n 45
 
7.9%
i 38
 
6.7%
G 36
 
6.3%
u 36
 
6.3%
d 36
 
6.3%
o 36
 
6.3%
e 23
 
4.0%
Other values (12) 130
22.9%

Family_Status
Categorical

Distinct5
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Married
31 
Together
24 
Single
Divorced
Widow
 
3

Length

Max length8
Median length7
Mean length7.2266667
Min length5

Characters and Unicode

Total characters542
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowWidow
4th rowMarried
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 31
41.3%
Together 24
32.0%
Single 9
 
12.0%
Divorced 8
 
10.7%
Widow 3
 
4.0%

Length

2025-07-25T21:45:24.738872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:24.907577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 31
41.3%
together 24
32.0%
single 9
 
12.0%
divorced 8
 
10.7%
widow 3
 
4.0%

Most occurring characters

ValueCountFrequency (%)
e 96
17.7%
r 94
17.3%
i 51
9.4%
d 42
7.7%
o 35
 
6.5%
g 33
 
6.1%
M 31
 
5.7%
a 31
 
5.7%
T 24
 
4.4%
t 24
 
4.4%
Other values (9) 81
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 96
17.7%
r 94
17.3%
i 51
9.4%
d 42
7.7%
o 35
 
6.5%
g 33
 
6.1%
M 31
 
5.7%
a 31
 
5.7%
T 24
 
4.4%
t 24
 
4.4%
Other values (9) 81
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 96
17.7%
r 94
17.3%
i 51
9.4%
d 42
7.7%
o 35
 
6.5%
g 33
 
6.1%
M 31
 
5.7%
a 31
 
5.7%
T 24
 
4.4%
t 24
 
4.4%
Other values (9) 81
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 96
17.7%
r 94
17.3%
i 51
9.4%
d 42
7.7%
o 35
 
6.5%
g 33
 
6.1%
M 31
 
5.7%
a 31
 
5.7%
T 24
 
4.4%
t 24
 
4.4%
Other values (9) 81
14.9%

Annual_Income
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50133.84
Minimum8820
Maximum85738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:25.142745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8820
5-th percentile19988.1
Q133832
median51876
Q364918.5
95-th percentile80775
Maximum85738
Range76918
Interquartile range (IQR)31086.5

Descriptive statistics

Standard deviation19862.369
Coefficient of variation (CV)0.39618687
Kurtosis-1.0018271
Mean50133.84
Median Absolute Deviation (MAD)15801
Skewness-0.035776589
Sum3760038
Variance3.9451371 × 108
MonotonicityNot monotonic
2025-07-25T21:45:25.444178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65846 1
 
1.3%
51876 1
 
1.3%
78427 1
 
1.3%
39791 1
 
1.3%
57136 1
 
1.3%
79800 1
 
1.3%
70971 1
 
1.3%
72570 1
 
1.3%
42033 1
 
1.3%
33419 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
8820 1
1.3%
14045 1
1.3%
16626 1
1.3%
19510 1
1.3%
20193 1
1.3%
22390 1
1.3%
22554 1
1.3%
23331 1
1.3%
23957 1
1.3%
24762 1
1.3%
ValueCountFrequency (%)
85738 1
1.3%
83145 1
1.3%
82582 1
1.3%
82427 1
1.3%
80067 1
1.3%
79800 1
1.3%
78427 1
1.3%
77981 1
1.3%
76140 1
1.3%
75794 1
1.3%

Kids_Count
Categorical

Distinct3
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
43 
1
28 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Length

2025-07-25T21:45:25.859835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:26.006898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43
57.3%
1 28
37.3%
2 4
 
5.3%

Teens_Count
Categorical

Distinct3
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
1
37 
0
36 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%

Length

2025-07-25T21:45:26.170096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:26.322699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%

Most occurring characters

ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 37
49.3%
0 36
48.0%
2 2
 
2.7%
Distinct70
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
Minimum2012-01-10 00:00:00
Maximum2014-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T21:45:26.534265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:26.874497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Last_Visit
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.373333
Minimum0
Maximum96
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:27.291219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q127.5
median51
Q376
95-th percentile92
Maximum96
Range96
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation28.264089
Coefficient of variation (CV)0.5610923
Kurtosis-1.1236822
Mean50.373333
Median Absolute Deviation (MAD)25
Skewness-0.092543893
Sum3778
Variance798.85874
MonotonicityNot monotonic
2025-07-25T21:45:27.573528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 3
 
4.0%
54 3
 
4.0%
18 3
 
4.0%
63 3
 
4.0%
16 2
 
2.7%
96 2
 
2.7%
38 2
 
2.7%
76 2
 
2.7%
67 2
 
2.7%
89 2
 
2.7%
Other values (41) 51
68.0%
ValueCountFrequency (%)
0 2
2.7%
1 1
 
1.3%
3 2
2.7%
8 2
2.7%
14 1
 
1.3%
16 2
2.7%
17 1
 
1.3%
18 3
4.0%
19 1
 
1.3%
20 1
 
1.3%
ValueCountFrequency (%)
96 2
2.7%
95 1
1.3%
92 2
2.7%
89 2
2.7%
88 1
1.3%
87 1
1.3%
86 2
2.7%
85 2
2.7%
83 1
1.3%
82 1
1.3%

Spent_Wines
Real number (ℝ)

High correlation  Zeros 

Distinct66
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.48
Minimum0
Maximum1099
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:27.863643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127.5
median135
Q3367.5
95-th percentile930.7
Maximum1099
Range1099
Interquartile range (IQR)340

Descriptive statistics

Standard deviation288.11852
Coefficient of variation (CV)1.1642093
Kurtosis1.2676314
Mean247.48
Median Absolute Deviation (MAD)125
Skewness1.4103676
Sum18561
Variance83012.28
MonotonicityNot monotonic
2025-07-25T21:45:28.217479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 3
 
4.0%
8 3
 
4.0%
5 2
 
2.7%
587 2
 
2.7%
11 2
 
2.7%
46 2
 
2.7%
135 2
 
2.7%
562 1
 
1.3%
99 1
 
1.3%
1060 1
 
1.3%
Other values (56) 56
74.7%
ValueCountFrequency (%)
0 1
 
1.3%
2 1
 
1.3%
4 1
 
1.3%
5 2
2.7%
6 1
 
1.3%
8 3
4.0%
9 1
 
1.3%
10 1
 
1.3%
11 2
2.7%
12 1
 
1.3%
ValueCountFrequency (%)
1099 1
1.3%
1060 1
1.3%
1001 1
1.3%
972 1
1.3%
913 1
1.3%
777 1
1.3%
754 1
1.3%
587 2
2.7%
586 1
1.3%
571 1
1.3%

Spent_Fruits
Real number (ℝ)

High correlation  Zeros 

Distinct35
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.786667
Minimum0
Maximum160
Zeros17
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:28.593625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q336
95-th percentile125.4
Maximum160
Range160
Interquartile range (IQR)35

Descriptive statistics

Standard deviation41.084387
Coefficient of variation (CV)1.4785648
Kurtosis2.3812242
Mean27.786667
Median Absolute Deviation (MAD)10
Skewness1.8175877
Sum2084
Variance1687.9268
MonotonicityNot monotonic
2025-07-25T21:45:28.915982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 17
22.7%
1 6
 
8.0%
4 5
 
6.7%
17 4
 
5.3%
36 3
 
4.0%
15 3
 
4.0%
2 3
 
4.0%
10 3
 
4.0%
6 2
 
2.7%
3 2
 
2.7%
Other values (25) 27
36.0%
ValueCountFrequency (%)
0 17
22.7%
1 6
 
8.0%
2 3
 
4.0%
3 2
 
2.7%
4 5
 
6.7%
6 2
 
2.7%
8 1
 
1.3%
10 3
 
4.0%
11 1
 
1.3%
12 1
 
1.3%
ValueCountFrequency (%)
160 1
1.3%
147 1
1.3%
140 1
1.3%
131 1
1.3%
123 1
1.3%
120 2
2.7%
97 1
1.3%
83 1
1.3%
81 1
1.3%
66 2
2.7%

Spent_Meat
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.08
Minimum2
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:29.194165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.7
Q119.5
median47
Q3187.5
95-th percentile633.4
Maximum898
Range896
Interquartile range (IQR)168

Descriptive statistics

Standard deviation225.14624
Coefficient of variation (CV)1.4333222
Kurtosis1.9450463
Mean157.08
Median Absolute Deviation (MAD)37
Skewness1.7359243
Sum11781
Variance50690.831
MonotonicityNot monotonic
2025-07-25T21:45:29.480358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3
 
4.0%
4 2
 
2.7%
30 2
 
2.7%
2 2
 
2.7%
10 2
 
2.7%
12 2
 
2.7%
44 2
 
2.7%
22 2
 
2.7%
5 2
 
2.7%
21 2
 
2.7%
Other values (52) 54
72.0%
ValueCountFrequency (%)
2 2
2.7%
4 2
2.7%
5 2
2.7%
7 2
2.7%
8 1
 
1.3%
9 1
 
1.3%
10 2
2.7%
11 3
4.0%
12 2
2.7%
13 1
 
1.3%
ValueCountFrequency (%)
898 1
1.3%
813 1
1.3%
731 1
1.3%
653 1
1.3%
625 1
1.3%
599 1
1.3%
595 1
1.3%
572 1
1.3%
550 1
1.3%
530 1
1.3%

Spent_Fish
Real number (ℝ)

High correlation  Zeros 

Distinct41
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.253333
Minimum0
Maximum247
Zeros13
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:30.094132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median16
Q333
95-th percentile118.1
Maximum247
Range247
Interquartile range (IQR)30

Descriptive statistics

Standard deviation44.352426
Coefficient of variation (CV)1.5161495
Kurtosis9.4842697
Mean29.253333
Median Absolute Deviation (MAD)14
Skewness2.8784742
Sum2194
Variance1967.1377
MonotonicityNot monotonic
2025-07-25T21:45:30.462378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 13
 
17.3%
3 5
 
6.7%
15 4
 
5.3%
2 4
 
5.3%
6 3
 
4.0%
17 3
 
4.0%
21 3
 
4.0%
8 2
 
2.7%
20 2
 
2.7%
4 2
 
2.7%
Other values (31) 34
45.3%
ValueCountFrequency (%)
0 13
17.3%
2 4
 
5.3%
3 5
 
6.7%
4 2
 
2.7%
6 3
 
4.0%
7 1
 
1.3%
8 2
 
2.7%
10 1
 
1.3%
12 1
 
1.3%
13 1
 
1.3%
ValueCountFrequency (%)
247 1
1.3%
180 1
1.3%
156 1
1.3%
151 1
1.3%
104 1
1.3%
93 1
1.3%
85 1
1.3%
80 1
1.3%
76 1
1.3%
63 1
1.3%

Spent_Sweets
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.413333
Minimum0
Maximum173
Zeros16
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:30.704493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q323
95-th percentile125.3
Maximum173
Range173
Interquartile range (IQR)22

Descriptive statistics

Standard deviation40.967717
Coefficient of variation (CV)1.6780878
Kurtosis3.5794725
Mean24.413333
Median Absolute Deviation (MAD)6
Skewness2.1264743
Sum1831
Variance1678.3539
MonotonicityNot monotonic
2025-07-25T21:45:30.953136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 16
21.3%
1 8
 
10.7%
2 8
 
10.7%
13 6
 
8.0%
26 3
 
4.0%
4 3
 
4.0%
40 2
 
2.7%
11 2
 
2.7%
20 2
 
2.7%
19 2
 
2.7%
Other values (21) 23
30.7%
ValueCountFrequency (%)
0 16
21.3%
1 8
10.7%
2 8
10.7%
3 2
 
2.7%
4 3
 
4.0%
6 1
 
1.3%
8 1
 
1.3%
10 2
 
2.7%
11 2
 
2.7%
12 1
 
1.3%
ValueCountFrequency (%)
173 1
1.3%
141 1
1.3%
137 1
1.3%
126 1
1.3%
125 1
1.3%
123 1
1.3%
118 1
1.3%
107 1
1.3%
87 1
1.3%
57 1
1.3%

Spent_Gold
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.08
Minimum0
Maximum241
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:31.229008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median26
Q363.5
95-th percentile201.6
Maximum241
Range241
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation60.24853
Coefficient of variation (CV)1.2030457
Kurtosis2.4977666
Mean50.08
Median Absolute Deviation (MAD)19
Skewness1.7703942
Sum3756
Variance3629.8854
MonotonicityNot monotonic
2025-07-25T21:45:31.670488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
7 4
 
5.3%
2 4
 
5.3%
13 4
 
5.3%
26 3
 
4.0%
42 3
 
4.0%
224 2
 
2.7%
4 2
 
2.7%
18 2
 
2.7%
21 2
 
2.7%
9 2
 
2.7%
Other values (39) 47
62.7%
ValueCountFrequency (%)
0 1
 
1.3%
1 2
2.7%
2 4
5.3%
3 2
2.7%
4 2
2.7%
5 2
2.7%
6 1
 
1.3%
7 4
5.3%
9 2
2.7%
11 1
 
1.3%
ValueCountFrequency (%)
241 1
1.3%
227 1
1.3%
224 2
2.7%
192 1
1.3%
159 1
1.3%
141 1
1.3%
138 1
1.3%
135 1
1.3%
127 1
1.3%
114 1
1.3%

Promo_Purchases
Real number (ℝ)

Distinct9
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4933333
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:31.897470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1205133
Coefficient of variation (CV)0.85047324
Kurtosis7.3763463
Mean2.4933333
Median Absolute Deviation (MAD)1
Skewness2.381454
Sum187
Variance4.4965766
MonotonicityNot monotonic
2025-07-25T21:45:32.092327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 33
44.0%
2 16
21.3%
4 9
 
12.0%
3 8
 
10.7%
5 4
 
5.3%
6 2
 
2.7%
7 1
 
1.3%
11 1
 
1.3%
12 1
 
1.3%
ValueCountFrequency (%)
1 33
44.0%
2 16
21.3%
3 8
 
10.7%
4 9
 
12.0%
5 4
 
5.3%
6 2
 
2.7%
7 1
 
1.3%
11 1
 
1.3%
12 1
 
1.3%
ValueCountFrequency (%)
12 1
 
1.3%
11 1
 
1.3%
7 1
 
1.3%
6 2
 
2.7%
5 4
 
5.3%
4 9
 
12.0%
3 8
 
10.7%
2 16
21.3%
1 33
44.0%

Web_Orders
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7466667
Minimum0
Maximum11
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:32.275733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3715278
Coefficient of variation (CV)0.63297006
Kurtosis0.95628221
Mean3.7466667
Median Absolute Deviation (MAD)1
Skewness1.0289826
Sum281
Variance5.6241441
MonotonicityNot monotonic
2025-07-25T21:45:32.457401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 20
26.7%
1 12
16.0%
2 10
13.3%
4 10
13.3%
5 7
 
9.3%
7 7
 
9.3%
6 3
 
4.0%
11 2
 
2.7%
8 2
 
2.7%
0 1
 
1.3%
ValueCountFrequency (%)
0 1
 
1.3%
1 12
16.0%
2 10
13.3%
3 20
26.7%
4 10
13.3%
5 7
 
9.3%
6 3
 
4.0%
7 7
 
9.3%
8 2
 
2.7%
9 1
 
1.3%
ValueCountFrequency (%)
11 2
 
2.7%
9 1
 
1.3%
8 2
 
2.7%
7 7
 
9.3%
6 3
 
4.0%
5 7
 
9.3%
4 10
13.3%
3 20
26.7%
2 10
13.3%
1 12
16.0%

Catalog_Orders
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6
Minimum0
Maximum11
Zeros21
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:32.638843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8945429
Coefficient of variation (CV)1.1132857
Kurtosis0.86163329
Mean2.6
Median Absolute Deviation (MAD)1
Skewness1.2564976
Sum195
Variance8.3783784
MonotonicityNot monotonic
2025-07-25T21:45:32.935578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 21
28.0%
1 17
22.7%
2 9
12.0%
3 6
 
8.0%
6 6
 
8.0%
5 5
 
6.7%
9 4
 
5.3%
4 4
 
5.3%
11 2
 
2.7%
7 1
 
1.3%
ValueCountFrequency (%)
0 21
28.0%
1 17
22.7%
2 9
12.0%
3 6
 
8.0%
4 4
 
5.3%
5 5
 
6.7%
6 6
 
8.0%
7 1
 
1.3%
9 4
 
5.3%
11 2
 
2.7%
ValueCountFrequency (%)
11 2
 
2.7%
9 4
 
5.3%
7 1
 
1.3%
6 6
 
8.0%
5 5
 
6.7%
4 4
 
5.3%
3 6
 
8.0%
2 9
12.0%
1 17
22.7%
0 21
28.0%

Store_Orders
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4933333
Minimum2
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:33.171021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q37
95-th percentile12
Maximum13
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9745467
Coefficient of variation (CV)0.54148301
Kurtosis0.32389756
Mean5.4933333
Median Absolute Deviation (MAD)1
Skewness1.1114826
Sum412
Variance8.8479279
MonotonicityNot monotonic
2025-07-25T21:45:33.374397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 18
24.0%
3 15
20.0%
6 8
10.7%
7 7
 
9.3%
5 7
 
9.3%
2 6
 
8.0%
12 4
 
5.3%
10 3
 
4.0%
8 2
 
2.7%
11 2
 
2.7%
Other values (2) 3
 
4.0%
ValueCountFrequency (%)
2 6
 
8.0%
3 15
20.0%
4 18
24.0%
5 7
 
9.3%
6 8
10.7%
7 7
 
9.3%
8 2
 
2.7%
9 1
 
1.3%
10 3
 
4.0%
11 2
 
2.7%
ValueCountFrequency (%)
13 2
 
2.7%
12 4
 
5.3%
11 2
 
2.7%
10 3
 
4.0%
9 1
 
1.3%
8 2
 
2.7%
7 7
 
9.3%
6 8
10.7%
5 7
 
9.3%
4 18
24.0%

Web_Visits
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-07-25T21:45:33.569005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3480872
Coefficient of variation (CV)0.45155523
Kurtosis-1.0183998
Mean5.2
Median Absolute Deviation (MAD)1
Skewness-0.38864421
Sum390
Variance5.5135135
MonotonicityNot monotonic
2025-07-25T21:45:33.767686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 16
21.3%
5 11
14.7%
6 11
14.7%
2 10
13.3%
8 9
12.0%
1 6
 
8.0%
4 5
 
6.7%
3 4
 
5.3%
9 3
 
4.0%
ValueCountFrequency (%)
1 6
 
8.0%
2 10
13.3%
3 4
 
5.3%
4 5
 
6.7%
5 11
14.7%
6 11
14.7%
7 16
21.3%
8 9
12.0%
9 3
 
4.0%
ValueCountFrequency (%)
9 3
 
4.0%
8 9
12.0%
7 16
21.3%
6 11
14.7%
5 11
14.7%
4 5
 
6.7%
3 4
 
5.3%
2 10
13.3%
1 6
 
8.0%

Campaign_1
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
69 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Length

2025-07-25T21:45:34.011242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:34.233854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69
92.0%
1 6
 
8.0%

Campaign_2
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
70 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Length

2025-07-25T21:45:34.450247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:34.604436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Campaign_3
Unsupported

Missing  Rejected  Unsupported 

Missing75
Missing (%)100.0%
Memory size732.0 B

Campaign_4
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
70 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Length

2025-07-25T21:45:34.777174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:34.922148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 70
93.3%
1 5
 
6.7%

Campaign_5
Categorical

Constant 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
100.0%

Length

2025-07-25T21:45:35.069521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:35.166243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 75
100.0%

Most occurring characters

ValueCountFrequency (%)
0 75
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Complaint_Flag
Categorical

Constant 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
100.0%

Length

2025-07-25T21:45:35.306516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:35.472885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 75
100.0%

Most occurring characters

ValueCountFrequency (%)
0 75
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75
100.0%

Contact_Cost
Categorical

Constant 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
3
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 75
100.0%

Length

2025-07-25T21:45:35.659706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:35.880019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 75
100.0%

Most occurring characters

ValueCountFrequency (%)
3 75
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 75
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 75
100.0%

Total_Revenue
Categorical

Constant 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
11
75 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters150
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 75
100.0%

Length

2025-07-25T21:45:36.050154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T21:45:36.189026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11 75
100.0%

Most occurring characters

ValueCountFrequency (%)
1 150
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 150
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 150
100.0%

Interactions

2025-07-25T21:45:18.647661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:51.843275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:55.300480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:58.833703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.827634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.993701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.191740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.497375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.623937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.972062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.008741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.965974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.091926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.056980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:15.399170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:18.851100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:52.158701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:55.507508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:59.035083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.894242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.073937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.277551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.559678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.700728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.042608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.075941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.033428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.155606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.116689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:15.588995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:19.045450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:52.401834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:55.708493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:59.246548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.966290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.148769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.364991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.624745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.784347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.116284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.134108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.102195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.218807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.173308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:15.815290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:19.254572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:52.656204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:55.973819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:59.488577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.037794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.241365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.577384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.693417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.882716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.196853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.198747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.176120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.293066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.236800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:16.279133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:19.439382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:52.900878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:56.187319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:59.760642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.115406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.324333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.654262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.757302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.962721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.260728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.255962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.242087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.353889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.303707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:16.483034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:19.750699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:53.137244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:56.427210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:00.048868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.182156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.398670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.731961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.826106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.034410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.329942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.313439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.308860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.420902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.362775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:16.695008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:19.987955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:53.353096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:56.732488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:00.344275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.263794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.474384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.815851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.908759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.123472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.412167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.376330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.389456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.493380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.466620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:16.932249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:20.166395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:53.530758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:56.968711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:00.656300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.350981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.551419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.890588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.972111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.198825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.487288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.431754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.455915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.559923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.696780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.102928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:20.364897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:53.726651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:57.189682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:00.955618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.436484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.626766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.967075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.033583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.269702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.557399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.487510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.645689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.624035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:13.899361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.270764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:20.578852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:53.942774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:57.434996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.304595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.534710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.708977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.055358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.116745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.344252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.621912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T21:45:12.692083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:14.168833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.453621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:20.755991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:54.210859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:57.694094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.393874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.610737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.781033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.129128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.219435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.455576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.680113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.625249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.776044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.755715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:14.394701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.632881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:21.024467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:54.432135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:57.956875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.477384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.686389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.863490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.200060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.298719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.537169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.743439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.691284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.836323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.817625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:14.647786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.811137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:21.273570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:54.666135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:58.170006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:02.586160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:03.761482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:04.936639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.280472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.376292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.608576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.812052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.755851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:11.895990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.871866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:14.837600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:17.986376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:21.461270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:44:54.854777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T21:45:06.347346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.457863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.678563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T21:45:10.818906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T21:45:15.024520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T21:45:03.909735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:05.106704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:06.422183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:07.532955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:08.892928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:09.937004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:10.889724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.019957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:12.992946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:15.203117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T21:45:18.417574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-25T21:45:36.359561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Annual_IncomeBirth_YearCampaign_1Campaign_2Campaign_4Catalog_OrdersEdu_LevelFamily_StatusKids_CountLast_VisitPromo_PurchasesSpent_FishSpent_FruitsSpent_GoldSpent_MeatSpent_SweetsSpent_WinesStore_OrdersTeens_CountUser_KeyWeb_OrdersWeb_Visits
Annual_Income1.000-0.2150.0000.1370.3640.8190.0000.2130.343-0.075-0.1140.5650.6170.5760.8080.4480.8580.7520.3700.1450.554-0.635
Birth_Year-0.2151.0000.0000.0000.096-0.3070.1900.0000.000-0.072-0.202-0.136-0.074-0.141-0.212-0.034-0.276-0.1270.284-0.019-0.1270.236
Campaign_10.0000.0001.0000.0000.1840.2960.0000.1910.0000.0000.0000.0000.0000.5550.0000.0000.0000.0000.2050.0000.0000.000
Campaign_20.1370.0000.0001.0000.0000.0000.0540.0000.0000.1990.2870.0000.0000.0000.0000.0000.4960.3700.0130.0000.5600.302
Campaign_40.3640.0960.1840.0001.0000.4970.0000.4660.1630.0000.0000.6270.2780.3280.5550.0000.5480.2610.0510.0000.0000.606
Catalog_Orders0.819-0.3070.2960.0000.4971.0000.0000.2690.244-0.0920.0060.6750.6660.7200.8620.5160.8630.6800.1480.0950.629-0.537
Edu_Level0.0000.1900.0000.0540.0000.0001.0000.0000.0000.0000.0000.0310.0860.1000.0000.0000.0000.1780.1540.1920.0000.231
Family_Status0.2130.0000.1910.0000.4660.2690.0001.0000.0000.0000.0000.2310.2190.0000.1910.0000.0000.1050.0000.1500.0000.286
Kids_Count0.3430.0000.0000.0000.1630.2440.0000.0001.0000.1210.1330.1980.1650.0000.0000.0530.2700.2660.0000.0000.2350.494
Last_Visit-0.075-0.0720.0000.1990.000-0.0920.0000.0000.1211.0000.062-0.0410.119-0.015-0.0720.060-0.126-0.0890.000-0.066-0.060-0.054
Promo_Purchases-0.114-0.2020.0000.2870.0000.0060.0000.0000.1330.0621.000-0.168-0.2690.051-0.044-0.0780.1370.0780.184-0.0510.3070.416
Spent_Fish0.565-0.1360.0000.0000.6270.6750.0310.2310.198-0.041-0.1681.0000.7750.6070.8060.5680.5260.5220.000-0.0460.483-0.477
Spent_Fruits0.617-0.0740.0000.0000.2780.6660.0860.2190.1650.119-0.2690.7751.0000.6290.7400.6340.4750.5610.000-0.0710.453-0.544
Spent_Gold0.576-0.1410.5550.0000.3280.7200.1000.0000.000-0.0150.0510.6070.6291.0000.6710.4370.6040.4370.0000.0810.610-0.273
Spent_Meat0.808-0.2120.0000.0000.5550.8620.0000.1910.000-0.072-0.0440.8060.7400.6711.0000.5470.8160.7500.000-0.0200.680-0.502
Spent_Sweets0.448-0.0340.0000.0000.0000.5160.0000.0000.0530.060-0.0780.5680.6340.4370.5471.0000.3560.5140.0000.0310.350-0.428
Spent_Wines0.858-0.2760.0000.4960.5480.8630.0000.0000.270-0.1260.1370.5260.4750.6040.8160.3561.0000.8020.0000.0820.722-0.419
Store_Orders0.752-0.1270.0000.3700.2610.6800.1780.1050.266-0.0890.0780.5220.5610.4370.7500.5140.8021.0000.000-0.0120.519-0.563
Teens_Count0.3700.2840.2050.0130.0510.1480.1540.0000.0000.0000.1840.0000.0000.0000.0000.0000.0000.0001.0000.2210.0000.000
User_Key0.145-0.0190.0000.0000.0000.0950.1920.1500.000-0.066-0.051-0.046-0.0710.081-0.0200.0310.082-0.0120.2211.0000.031-0.085
Web_Orders0.554-0.1270.0000.5600.0000.6290.0000.0000.235-0.0600.3070.4830.4530.6100.6800.3500.7220.5190.0000.0311.0000.042
Web_Visits-0.6350.2360.0000.3020.606-0.5370.2310.2860.494-0.0540.416-0.477-0.544-0.273-0.502-0.428-0.419-0.5630.000-0.0850.0421.000

Missing values

2025-07-25T21:45:22.042243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-25T21:45:22.586693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_Revenue
093701945PhDMarried658460017-05-201368562812768040811636400NaN000311
146821958GraduationMarried518760015-10-2013889927102284861228100NaN000311
245301948MasterWidow784270024-10-201236972195951802613833710300NaN100311
382121971MasterMarried397910128-03-2013898515271313212314700NaN000311
464091967GraduationDivorced571360018-05-20131826714059934121271757600NaN000311
590581955GraduationWidow798000023-09-20126510602153032022415115310NaN100311
642991960GraduationTogether709710121-09-2012281001175729312517711115700NaN000311
7104131984GraduationMarried725700025-04-2014672748321615114122414612100NaN000311
8189019712n CycleTogether420331119-09-20129511142071102700NaN000311
984141962PhDSingle334190117-08-2013765601200182204700NaN000311
User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_Revenue
65101291966GraduationTogether824270012/3/20143548214750910410710713512100NaN000311
665501952GraduationDivorced623350123-05-2013872431312178562623313200NaN000311
6710451965GraduationTogether521170116-08-20125511210107300202524700NaN000311
6843771971GraduationMarried52914017/1/201332254104430102272733710NaN000311
6950311974GraduationTogether831450022-09-201214777357313913711415911200NaN000311
7014041968GraduationTogether349162015-05-201389512382330424513900NaN000311
7158831972GraduationMarried779811026-05-20137813812020416126603747500NaN000311
7257941974PhDMarried463740117-03-2014140802100173717801NaN100311
7326121987GraduationMarried757940024-12-20133375416062563324817512300NaN100311
7441491948PhDTogether761400013-05-20145758666653170261596200NaN000311